Efficient Sensor Network Research Articles

An energy efficient authentication scheme for cluster-based wireless IoT sensor networks

Wireless Internet of Things (IoT) sensor networks (WITSN) play a pivotal role in modern society, facilitating a myriad of applications ranging from smart homes to industrial automation. Therefore, safeguarding these networks against security threats is paramount to ensure the integrity, confidentiality, and availability of data transmitted within them. However, WITSNs face escalating security threats due to their diverse structures and platforms. Existing literature has identified these vulnerabilities but lacks comprehensive solutions to address them effectively. To bridge this gap, this paper proposes a novel security approach termed Randomized Bi-Phase Authentication Scheme (RBAS), which integrates digital watermarking techniques to fortify both external and internal network security. RBAS not only tackles data availability, confidentiality, and authenticity challenges prevalent in WITSNs but also strives to maintain a delicate equilibrium between robust security measures and energy efficiency. Key contributions of this work include the meticulous examination and validation of the integration of cyclic redundancy check (CRC) codes in IoT sensor network authentication, demonstrating their efficacy in error detection through simulations. Furthermore, RBAS employs advanced hashing, cryptography, and dynamic verification codes to ensure strong error detection and data authenticity, leveraging robust CRC codes and randomization to thwart potential attacks. The scheme's complexity acts as a deterrent against manipulation, while its cluster awareness enhances adaptability, and cryptographic principles bolster overall security. Extensive performance evaluation using the Network Simulator-2 (NS2) reveals significant benefits of RBAS, including an 8 % reduction in power consumption and a 7 % increase in network longevity. Notably, RBAS surpasses existing solutions with a 14 % reduction in dropping ratio and an 8 % decrease in latency. The success of RBAS stems from its innovative utilization of lightweight watermarking techniques and cluster-based routing, enabling proactive identification of data tampering and thereby enhancing the network's overall security posture. This pioneering work not only advances the state of the art in WITSN security but also holds profound implications for the practical deployment of secure and efficient IoT sensor networks in real-world scenarios.

Artificial Intelligence: A New Paradigm for Distributed Sensor Networks on the Internet of Things: A Review

The confluence of Artificial Intelligence (AI) with the Internet of Things (IoT) has created new opportunities for distributed sensor networks in a variety of sectors. The potential of AI as a new paradigm for distributed sensor networks in the IoT is investigated in this review article, with an emphasis on the convergence of architecture, methodologies, platforms, sensors, devices, energy approaches, communication and networking, and applications. An analysis was conducted to examine the existing research in this field using a comprehensive literature review, revealing notable advancements, and indicating the need for further investigation. Furthermore, Moreover, suggestions and ideas are presented regarding potential innovation within the sector, with a particular emphasis on the imperative for further research and development. Our findings highlight AIoT's transformational ability in allowing more efficient and intelligent sensor networks, with implications for smart homes, healthcare, environmental monitoring, and industrial automation. The potential for harnessing the power of AI holds the key to unlocking novel opportunities and addressing the challenges inherent in dispersed sensor networks within the realm of the Internet of Things (IoT). This research advances our understanding of AIoT convergence and lays the groundwork for future breakthroughs in this interesting topic.

Spectrum Efficient Cognitive Radio Sensor Network for IoT with Low Energy Consumption

Cognitive Radio Sensor Networks (CRSNs) have emerged as a promising solution for efficient utilization of the limited frequency spectrum. One of the key challenges in CRSNs is achieving spectrum efficiency by avoiding interference and maximizing the use of the available spectrum. Particle Swarm Optimization (PSO) techniques have been widely used to optimize the spectrum allocation and improve the spectrum efficiency of CRSNs. In this paper the study provides an overview of the research on spectrum efficiency in CRSNs using PSO techniques and also discussed the key parameters that affect the spectrum efficiency, such as the swarm size, sensor's threshold and maximum number of iterations and highlights the importance of identifying the optimal combination of these parameters. This paper also emphasizes the need for further research and development in this area to improve the efficiency and effectiveness of PSO-based optimization techniques for CRSNs and to adapt them to various real-world scenarios. Achieving spectrum efficiency in CRSNs is critical for enabling effective wireless communication systems and improving the overall utilization of the available frequency spectrum.

Evaluation of Blockchain Networks’ Scalability Limitations in Low-Powered Internet of Things (IoT) Sensor Networks

With the development of Internet of Things (IoT) technologies, industries such as healthcare have started using low-powered sensor-based devices. Because IoT devices are typically low-powered, they are susceptible to cyber intrusions. As an emerging information security solution, blockchain technology has considerable potential for protecting low-powered IoT end devices. Blockchain technology provides promising security features such as cryptography, hash functions, time stamps, and a distributed ledger function. Therefore, blockchain technology can be a robust security technology for securing IoT low-powered devices. However, the integration of blockchain and IoT technologies raises a number of research questions. Scalability is one of the most significant. Blockchain’ scalability of low-powered sensor networks needs to be evaluated to identify the practical application of both technologies in low-powered sensor networks. In this paper, we analyse the scalability limitations of three commonly used blockchain algorithms running on low-powered single-board computers communicating in a wireless sensor network. We assess the scalability limitations of three blockchain networks as we increase the number of nodes. Our analysis shows considerable scalability variations between three blockchain networks. The results indicate that some blockchain networks can have over 800 ms network latency and some blockchain networks may use a bandwidth over 1600 Kbps. This work will contribute to developing efficient blockchain-based IoT sensor networks.

Enhanced energy efficient sleep awake aware intelligent sensor network routing protocol for wireless sensor network

One of the major challenge in Wireless Sensor Networks (WSN’s) deployment is efficient energy consumption. Critical distance, proper routing algorithm and error control coding techniques can be used for energy optimization. As WSN contains a large number of power constrained sensors, the sensed data from the environment should be transmitted in a cooperative way to the base station (BS). The pattern of the clustering structure can extend the sensor network life time, reduce the total consumed energy and regulate the data transmission. Clustering concept combines group of sensors which are located in the same communication range. Some of the routing protocol like, SEED, LEACH, SEP, Z-SEP etc., suffers from idle listening problem, which cannot cope with an environment with sensor nodes. It leads to energy wastage across the network. To manage energy efficiency and traffic heterogeneity issues, a new routing protocol called enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA) is proposed. Here, one sensor in each group will be in active mode whereas other sensors entered in sleep mode. Based on the nodes energy, sleep and awake node pairs will be altered. In the proposed method, one slot is allotted for group of pairs. The proposed approach is evaluated and compared against LEACH, SEP and Z-SEP protocols. Simulation results show that EEESAA protocol performs better than LEACH, SEP, Z-SEP in terms of cluster head selection, throughput, number of alive & dead nodes and network lifetime.

Optimization of wireless sensor networks energy consumption by the clustering method based on the firefly algorithm

Wireless sensor networks (WSNs) contain an inordinate number of sensor nodes that are spatially distributed. The network is composed of entities that determine its lifetime. The WSN nodes are equipped with a battery whose autonomy is limited in duration. In this paper, different solutions are introduced to improve the overall energy consumption of the network in order to improve its lifetime. Contrary to many works considering the clustering algorithm as one potential candidate to improve the network's lifetime, this study has investigated the firefly algorithm optimization where an optimal cluster head is selected from a group of nodes. The set-up process of the cluster head is based on a set of conditions. To measure the performance of the proposed approach, the number of dead nodes and data packets received by the base station (BS) or sink node are considered. The results are tested on 100 nodes for 5000 transmission rounds, the amount of data transported is 20 million bits a little more than the other methods. It has been shown that the proposed solution outperformed the traditional low energy adaptive clustering hierarchy (LEACH), threshold sensitive energy efficient sensor network (TEEN), and developed distributed energy-efficient clustering (DEEC) approaches.

Minimizing Energy Hole Problem Comparisons in Some Hierarchical WSN Routing Protocols

This paper study the efficient use of energy as a challenging task of designing these protocols. The energy holes are created due to non-uniform node distribution in the network when quickly drain the energy in a few nodes. It is studies removing energy holes by using sleep and awake mechanism for sensor nodes to save energy. This approach finds the maximum distance nodes to calculate the maximum energy for data. It intends to enumerate the dead nodes transmission, alive nodes, throughput, overhead and packet delivery ratio consumed by the entire network and affected by the network area changes. lastly, a brief performance analysis of Low Energy Adaptive Clustering Hierarchy (LEACH), Distributed Energy-Efficient Clustering (DEEC), Threshold sensitive Energy Efficient sensor Network protocol (TEEN) and Stable Election Protocol (SEP) is carried out considering metrics of the previous characteristics of wireless sensor network.

Adaptive Threshold Residual Energy‐Based Efficient Sensor Network Protocol (ATREEN)

Wireless sensor network (WSN) hierarchical (clustering) routing involves the gathering of sensor nodes to form clusters in which sensor node communicates to cluster head (CH). The CH is responsible for data aggregation and forwarding collected data toward base station (BS). Energy expenditure in usual protocols becomes unequal because of clusters having diverse lengths. In this paper, we propose another new enhanced clustering protocol called adaptive threshold residual energy‐based efficient sensor network protocol (ATREEN) for adaptive selection of CH in WSNs. In ATREEN, CHs are chosen by comparing threshold of node value with respect to distance between cluster member and CH, residual energy of sensor nodes, and position of sensor node with respect to BS. Simulation results show that ATREEN protocol extends network lifetime and stability with decline in energy dissipation compared to clustering protocols such as low‐energy adaptive clustering hierarchy (LEACH), power‐efficient gathering in sensor information systems (PEGASIS), and distributed energy‐efficient clustering (DEEC) scheme for heterogeneous WSNs.

Improved trustworthy, speed, and energy-efficient GPSR routing algorithm in large-scale WSN

Geographic routing networks broadcast their readiness to modify the routing protocol using the traditional methodology in wireless sensor network (WSN). These are effectively dealt with to preserve electricity. Researchers propose the Modified - Greedy Perimeter Stateless Routing (GPSR) system for efficient sensor network connection to determine the optimal path based on energy usage and avoids malicious activity. Depending on the limited memory nodes and battery power, rapid dynamic topology, and unpredictable equipment, the deployment of GPSR in sensor networks faces several challenges. The cost of communication across the network is close to ideal because of our Modified-GPSR method. The simulation results show that minimizing power and latency considerably increases the durability of the sensor node system. The study also focuses on estimating implementation costs and learning about design, implementation, and statistical practice.

Energy Efficient Routing and Dynamic Cluster Head Selection Using Enhanced Optimization Algorithms for Wireless Sensor Networks

A large number of spatially dispersed nodes on the wireless network create Wireless Sensor Networks (WSNs) to collect and analyze the physical data from the environment. The issues that affected the network and had an impact on network energy consumption were cluster head random selection, working node redundancy, and cluster head transmission path construction. Consequently, this energy constraint also has an impact on the network lifetime and energy-efficient routing. Therefore, the primary goals of this research are to decrease energy consumption and lengthen the network’s lifespan. So, using improved optimization algorithms, this paper presents a dynamic cluster head-based energy-efficient routing system. The Improved Coyote Optimization Algorithm (ICOA), in this case, consists of three phases setup, transmission, and measurement phase. The Improved Jaya Optimization Algorithm with Levy Flight (IJO-LF) then determines the route between the BS and the CH. It selects the most effective course based on the distance, node degree, and remaining energy. The proposed approach is compared with traditional methods and the routing protocols Power-Efficient Gathering in Sensor Information Systems (PEGASIS) and Threshold sensitive Energy Efficient Sensor Network protocol (TEEN) during implementation on the MATLAB platform. Performance indicators for the suggested methodology are evaluated based on data packets collected by the BS, energy usage, alive nodes, and dead nodes. The outputs of the suggested methodology performed better than the conventional plans.

THWSN: Enhanced Energy-Efficient Clustering Approach for Three-Tier Heterogeneous Wireless Sensor Networks

Heterogeneous wireless sensor networks (HWSNs) provide realistic application design capabilities. The sensor nodes (SNs) are arranged in groups or clusters. Each cluster has a cluster head (CH). The CH accumulates the sensed data from its SNs to be sent to a base station (BS). SNs have been positioned with batteries, which cannot be replaced. Energy consumption is a major concern for WSNs. In order to achieve efficient mission-critical sensor networks, it is necessary to develop energy-efficient routing protocols. We suggest an innovative and inventive approach to design an enhanced energy-efficient clustering approach (EEECA) to increase the network lifetime and reduce the energy consumption in HWSNs. The proposed protocol is based on the three-tier nodes, namely, standard, intermediate, and advanced nodes. In the clustering procedure, we consider various parameters for selecting the CH, namely, the initial energy of the SNs, the number of alive/dead nodes, and the remaining energy. Extensive performance parameters show that the proposed EEECA-THWSN extends the network lifetime by 25% and decreases the network’s energy consumption. The proposed protocol improves the energy efficiency of EEECA-THWSN and significantly increases the network throughput, network lifetime, and network stability.

Analysis of Energy Efficient Techniques of WSN

The wireless sensor networks is the decentralized type of network in which sensor devices sense information and pass it to base station. Due to dynamic topology of the network security, data aggregation and energy consumption are the major issue which affects network performance. In the past years many techniques are designed to improve lifetime of lifetime of sensor networks. In this review paper, energy efficient techniques of wireless sensor networks are reviewed in terms of certain parameters

ESSD: Energy Saving and Securing Data Algorithm for WSNs Security

The Wireless Sensor Networks (WSNs) are characterized by their widespread deployment due to low cost, but the WSNs are vulnerable to various types of attacks. To defend against the attacks, an effective security solution is required. However, the limits of these networks’ battery-based energy to the sensor are the most critical impediments to selecting cryptographic techniques. Consequently, finding a suitable algorithm that achieves the least energy consumption in data encryption and decryption and providing a highly protected system for data remains the fundamental problem. In this research, the main objective is to obtain data security during transmission by proposing a robust and low-power encryption algorithm, in addition, to examining security algorithms such as ECC and MD5 based on previous studies. In this research, the Energy Saving and Securing Data algorithm (ESSD) algorithm is introduced, which provides the Message Digest 5 (MD5) computation simplicity by modifying the Elliptic Curve Cryptography (ECC) under the primary condition of power consumption. These three algorithms, ECC, MD5, and ESSD, are applied to Low Energy Adaptive Clustering Hierarchy (LEACH) and Threshold-sensitive Energy Efficient Sensor Network Protocol (TEEN) hierarchical routing algorithms which are considered the most widely used in WSNs. The results of security methods under the LEACH protocol show that all nodes are dead at 456, 496, and 496, respectively, to ECC, MD5, and ESSD. The results of security methods under the TEEN protocol show that the test ends at 3743, 4815, and 4889, respectively, to ECC, MD5, and ESSD. Based on these results, the ESSD outperforms better in terms of increased security and less power consumption. In addition, it is advantageous when applied to TEEN protocol.

OPTIMIZATION OF PLATE RECOGNITION SENSOR LOCATIONS: A CASE STUDY IN TURKEY

With the advances in sensor and data transfer technologies, the usage areas of Automatic License Plate Recognition (ALPR) systems have been expanded in the public and private sectors. In public safety, ALPR systems are used to monitor and control traffic data at both individual and collective levels. To build an efficient sensor network, the locations of ALPR systems should be determined optimally. This study provides an approach to determine optimal locations of ALPR systems that maximize network coverage consisting of two measures: i) vehicle coverage and ii) road coverage. The former represents the daily average vehicle flow whereas the latter stands for the number of road-links covered. The relative importance of vehicle and road coverages are taken into consideration, and optimal solutions under various scenarios are presented. A close neighbor constraint is introduced to avoid inefficient distribution of ALPR systems on the network. A case study with numerical examples designed for two cities in Turkey is provided. The centralized and decentralized solutions are compared against the current state, and the results show that the network coverage increases substantially in the centralized case.

WITHDRAWN: Computational efficient wearable sensor network health monitoring system for sports athletics using IoT

Abstract The rapid development and growth in the Internet of Things (IoT) area offer great potential in the healthcare sector. Currently, wireless technology, fitness trackers, and body sensors in the sports field have a major impact on living efficiency and health system reliability. Wearable devices have become increasingly interested in measuring physiological factors, promoting health, and improving adherence to the practice in different populations from elite athletes to patients. In this paper, fog assisted Computational efficient Wearable sensor networks (FCE-WSN) has been proposed in health monitoring systems for sports athletic using IoT. The wearable device for the continuous real-time monitoring of heart rate, respiratory frequency, and movement cadence during physical activity has been analyzed. Besides, the sensor collected data is uploaded to the IoT-connection system Ethernet module, and the Authorized Individual accesses the data via the internet to track the athletics health. Moreover, the wearable device's analytical model and its use illustrate how computing resources' costs can be minimized while preserving health requests for access to medical information stored in a fog and cloud distribution setting. Our assessment system depends on a queue and can estimate the minimum computing resources needed to achieve the Service Level Agreement (SLA) (both fog and cloud nodes). The experimental results show that the proposed method is user-friendly, reliable, and economical to use regularly.

Energy efficient routing in wireless sensor network based on mobile sink guided by stochastic hill climbing

In Wireless Sensor Networks (WSNs), the reduction of energy consumption in the batteries of a sensor node is an important task. Sensor nodes of WSNs perform three significant functions such as data sensing, data transmitting and data relaying. Routing technique is one of the methods to enhance the sensor nodes battery lifetime. Energy optimization is done by using one of the heuristic routing methods for sensing and transmitting the data. To enhance the energy optimization mainly concentrated on data relaying. In this work stochastic hill climbing is adapted. The proposed solution for data relaying utilizes geographical routing and mobile sink technique. The sink collects the data from cluster heads and movement of the sink is routed by stochastic hill climbing. Network simulator 2 is used for experimentation purpose. This work also compares with the existing routing protocols like Energy-efficient Low Duty Cycle (ELDC), Threshold sensitive Energy Efficient sensor Network (TEEN) and Adaptive clustering protocol. The proposed work shows promising results with respect to lifetime, average energy of nodes and packet delivery ratio.

POSE.R

The article presents a distributed algorithm, called Prediction-based Opportunistic Sensing for Resilient and Efficient Sensor Networks (POSE.R), where the sensor nodes utilize predictions of the targets' positions to probabilistically control their multi-modal operating states to track the targets. There are two desired features of the algorithm: energy efficiency and resilience. If the target is traveling through a high-node-density area, then an optimal sensor selection approach is employed that maximizes a joint cost function of remaining energy and geometric diversity around the target’s position. This provides energy efficiency and increases the network lifetime while preventing redundant nodes from tracking the target. However, if the target is traveling through a low-node-density area or in a coverage gap (e.g., formed by node failures or non-uniform node deployment), then a potential game is played amongst the surrounding nodes to optimally expand their sensing ranges via minimizing energy consumption and maximizing target coverage. This provides resilience, that is, the self-healing capability to track the target in the presence of low node densities and coverage gaps. The algorithm is comparatively evaluated against existing approaches through Monte Carlo simulations that demonstrate its superiority in terms of tracking performance, network-resilience, and network-lifetime.

Online acoustic emission source localization in concrete structures using iterative and evolutionary algorithms

This study is motivated by the need to develop an efficient online structural health monitoring (SHM) framework to accurately localize damage induced acoustic emission (AE) sources in concrete structures. Experimental studies are carried out in concrete slabs considering pencil lead break (PLB) as artificial damage source to initialize acoustic emission (AE) waves. A simplified yet robust Iterative Planar Source (IPS) localization algorithm based on arrival time (ToA) is proposed first to identify arbitrarily selected several damage source locations for (1) rectangular (2)circular, and (3) zig-zag distributed AE sensor network arrangements. The results of the proposed localization algorithm are then compared with those obtained from an evolutionary particle-swarm optimization (PSO) algorithm for each sensor network arrangement to assess the performance of the AE source monitoring strategy. It is found that the zig-zag arrangement of the distributed AE sensors is the most efficient sensor network arrangement for damage source localization. The obtained results clearly represent the accuracy and robustness of the proposed online monitoring framework for localizing damage induced acoustic emission sources in concrete structures without extensive manual intervention.

Energy Efficient Routing in Wireless Sensor Network Based on Composite Fuzzy Methods

Optimization of energy consumption in the batteries of a sensor node plays an essential role in wireless Sensor networks (WSNs). The longevity of sensor nodes depends on efficiency of energy utilization in batteries. Energy is consumed by sensor nodes in WSNs to perform three significant functions namely data sensing, transmitting and relaying. The battery energy in WSNs depletes mainly due to sampling rate and transmission rate. In the present work, the most important parameters affecting the longevity of network are indentified by modeling the energy consumption. The parameters are expressed as a fuzzy membership function of variables affecting the life time of network. Fuzzy logic is used at multiple levels to optimize the parameters. Network simulator-2 is used for experimentation purpose. The proposed work is also compared with the existing routing protocols like Enhanced Low Duty Cycle, Threshold Sensitive Energy Efficient Sensor Network and Distributed Energy Efficient Adaptive Clustering Protocol with Data Gathering. The proposed solution is found to be more energy efficient and hence ensures longer network lifetime.

A Novel Real-Time Deep Learning Approach for Indoor Localization Based on RF Environment Identification

Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly enhanced by identifying the type of the surrounding indoor environment. This letter develops a highly accurate and computationally efficient method for indoor localization based on a novel two-stage deep learning approach. In the first stage, a convolutional neural network (CNN) is designed to perform indoor environment identification by extracting the inherent features based on a real dataset obtained from radio-frequency (RF) measurements. Then, in the second stage, another CNN is designed to perform indoor localization taking into account knowledge of the environment type as obtained from the first stage. Numerical investigations demonstrate that the proposed two-stage CNN deep learning approach outperforms benchmark methods, with significant enhancements in localization accuracy and computational efficiency. The findings of this letter make the proposed CNN approach a key element for facilitating real-time deployment of efficient low-power IoT sensor networks.